Quantitative investigation of dose accumulation errors from intra-fraction motion in MRgRT for prostate cancer

Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction

Background and purpose

MRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC.

Materials and methods

An in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes.

Results

The planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%.

Conclusion

The straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

Technical note: Intensity-based quality assurance criteria for deformable image registration in image-guided radiotherapy

BACKGROUND

Deformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissue boundaries.

PURPOSE

The purpose of this study is to investigate the existing intensity-based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour-based quality assurance criteria.

METHODS

All criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial information.

RESULTS

We found that besides being fast and operator-independent, the intensity-based criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher gamma pass rate of the predicted registration error than commonly used spatial quality assurance criteria.

CONCLUSIONS

Intensity-based quality assurance criteria can provide the required confidence in decisions about using mono-modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapy treatments.

Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy

Background and Purpose

Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose.

Materials and Methods

The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated.

Results

The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0.

Conclusions

Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases.

Development of an abdominal dose accumulation tool and assessments of accumulated dose in gastrointestinal organs

Objective.Online adaptive radiotherapy has demonstrated improved dose conformality in response to inter-fraction geometric variations in the abdomen. The dosimetric impact of intra-fractional variations in anatomic configuration resulting from breathing, gastric contraction and slow configuration motion, however, have been largely ignored, leading to differences between delivered and planned. To investigate the impact of intra-fractional abdominal motions on delivered dose, anatomical deformations due to these three motion modes were extracted from dynamic MRI data using a previously developed hierarchical motion modeling methodology.Approach. Motion magnitudes were extracted from deformation fields between a reference state and all other motion states of the patient. Delivered dose estimates to various gastrointestinal organs (stomach, duodenum, small bowel and colon) were calculated on each motion state of the patient and accumulated to estimate the delivered dose to each organ for the entire treatment fraction.Main results. Across a sample of 10 patients, maximal motions of 33.6, 33.4, 47.6 and 49.2 mm were observed over 20 min for the stomach, duodenum, small bowel and colon respectively. Dose accumulation results showed that motions could lead to average increases of 2.0, 2.1, 1.1, 0.7 Gy to the maximum dose to 0.5cc (D0.5cc) and 3.0, 2.5, 1.3, 0.9 Gy to the maximum dose to 0.1cc (D0.1cc) for these organs at risk. From the 40 dose accumulations performed (10 for each organ at risk), 27 showed increases of modeled delivered dose compared to planned doses, 4 of which exceeded planned dose constraints.Significance. The use of intra-fraction motion measurements to accumulate delivered doses is feasible, and supports retrospective estimation of dose delivery to improve estimates of delivered doses, and further guide strategies for both plan adaptation as well as advances in intra-fraction motion management.

Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI

BACKGROUND AND PURPOSE

In MR-guided SBRT of pancreatic cancer, intrafraction motion is typically monitored with (interleaved) 2D cine MRI. However, tumor surroundings are often not fully captured in these images, and motion might be distorted by through-plane movement. In this study, the feasibility of highly accelerated 3D cine MRI to reconstruct the delivered dose during MR-guided SBRT was assessed.

MATERIALS AND METHODS

A 3D cine MRI sequence was developed for fast, time-resolved 4D imaging, featuring a low spatial resolution that allows for rapid volumetric imaging at 430 ms. The 3D cines were acquired during the entire beam-on time of 23 fractions of online adaptive MR-guided SBRT for pancreatic tumors on a 1.5 T MR-Linac. A 3D deformation vector field (DVF) was extracted for every cine dynamic using deformable image registration. Next, these DVFs were used to warp the partial dose delivered in the time interval between consecutive cine acquisitions. The warped dose plans were summed to obtain a total delivered dose. The delivered dose was also calculated under various motion correction strategies. Key DVH parameters of the GTV, duodenum, small bowel and stomach were extracted from the delivered dose and compared to the planned dose. The uncertainty of the calculated DVFs was determined with the inverse consistency error (ICE) in the high-dose regions.

RESULTS

The mean (SD) relative (ratio delivered/planned) D_99% of the GTV was 0.94 (0.06), and the mean (SD) relative D_0.5cc of the duodenum, small bowel, and stomach were respectively 0.98 (0.04), 1.00 (0.07), and 0.98 (0.06). In the fractions with the lowest delivered tumor coverage, it was found that significant lateral drifts had occurred. The DVFs used for dose warping had a low uncertainty with a mean (SD) ICE of 0.65 (0.07) mm.

CONCLUSION

We employed a fast, real-time 3D cine MRI sequence for dose reconstruction in the upper abdomen, and demonstrated that accurate DVFs, acquired directly from these images, can be used for dose warping. The reconstructed delivered dose showed only a modest degradation of tumor coverage, mostly attainable to baseline drifts. This emphasizes the need for motion monitoring and development of intrafraction treatment adaptation solutions, such as baseline drift corrections.

Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent

BACKGROUND AND PURPOSE

Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT.

MATERIALS AND METHODS

We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course.

RESULTS

The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D_95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found.

CONCLUSION

Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.

Accumulated bladder wall dose is correlated with patient-reported acute urinary toxicity in prostate cancer patients treated with stereotactic, daily adaptive MR-guided radiotherapy

BACKGROUND AND PURPOSE

Magnetic resonance (MR)-guided linear accelerators (MR-Linac) enable accurate estimation of delivered doses through dose accumulation using daily MR images and treatment plans. We aimed to assess the association between the accumulated bladder (wall) dose and patient-reported acute urinary toxicity in prostate cancer (PCa) patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

One-hundred-and-thirty PCa patients treated on a 1.5T MR-Linac were included. Patients filled out International Prostate Symptom Scores (IPSS) questionnaires at baseline, 1 month, and 3 months post-treatment. Deformable image registration-based dose accumulation was performed to reconstruct the delivered dose. Dose parameters for both bladder and bladder wall were correlated with a clinically relevant increase in IPSS (≥10 points) and/or start of alpha-blockers within 3 months using logistic regression.

RESULTS

Thirty-nine patients (30%) experienced a clinically relevant IPSS increase and/or started with alpha-blockers. Bladder D5cm³, V_10-35Gy (in %), and D_mean and Bladder wall V_10-35Gy (cm³ and %) and D_mean were correlated with the outcome (odds ratios 1.04-1.33, p-values 0.001-0.044). Corrected for baseline characteristics, bladder V_10-35Gy (in %) and D_mean and bladder wall V_10-35Gy (cm³ and %) and D_mean were still correlated with the outcome (odds ratios 1.04-1.30, p-values 0.001-0.028). Bladder wall parameters generally showed larger AUC values.

CONCLUSION

This is the first study to assess the correlation between accumulated bladder wall dose and patient-reported urinary toxicity in PCa patients treated with MR-guided SBRT. The dose to the bladder wall is a promising parameter for prediction of patient-reported urinary toxicity and therefore warrants prospective validation and consideration in treatment planning.